MetaFlux

MetaFlux is a we-based tool for

• visualizing
• interacting
• creating
• comparing

genome scale metabolic models of microbial consortia.

What is iGEM?

University of Toronto iGEM (international Genetically Engineered Machine) is a student association dedicated to the practice of synthetic biology and dissemination of its scientific foundations. The culmination of each year's efforts is a submission to the iGEM conferences as the University of Toronto team.

Usage

Straightforward drag and drop UI, ability to add species, and analazye their reactions and metabolites, along side calculating their flux.

Installation

To be able to use our web app, you must have NodeJS and the latest version of npm. Following code assumes a Debian based OS.

$ sudo npm install -g bower
$ git clone https://github.com/igemuoftATG/fba-webapp
$ cd fba-webapp
$ npm install
$ bower install
$ gulp

Changelog

1.4.4

• Renders metabolic network as a set of nodes and links
• Integration of d3 with custom canvas rendering for improved performance
• Basic dragging of nodes working
• Panning and zooming of canvas working
• (1.1.x) Added deletion and adding, beta and WIP
  • removal of nodes from target/source when trying to create a reaction is now removed from the options menu
  • (1.1.1) Error handling
  • (1.1.2) Significant performance increases
• (1.2.x) Re factored code for an MVC model, significant overhaul changes
• (1.3.x)
  • Added the specie extracellular network and added new specie class that extends node
  • (1.3.1) Fixed reaction visual bug
• (1.4.x) You can now enter a specie from a network
  • (1.4.1) Fixed broken adding/populate options. Few more modular improvments
  • (1.4.2) Made it more modular when it comes to adding new systems
  • (1.4.3) Added the ability to return to the network
  • (1.4.4) Zooming in and out of the specie/system returns to earlier state

ToDos

As these items are completed, add them to the changelog with the appropriate version. Each task completion warrants a micro versioning. (major.minor.micro). When a set of tasks warrants a feature completion, that will warrant a minor versioning. Major versioning changes will only occur under the condition that the API has change, that is, 2.0.0 implies that API calls from 1.X.X may be deprecated and no longer guaranteed to work.

Front-End

• Add node
  • Ability to fill in all properties so that it can be stored as a valid COBRApy Metabolite or Reaction in JSON format
• Delete node
  • Prepare a model, that is, the object representing the array of Metabolite's and Reaction's without the node, to be sent off the back-end for FBA
• Add/Delete Reaction
  • See above. Must follow COBRApy standards.
• Subspace partitioning
  • May either use forces within forces, group of nodes acting as one node, etc
  • Need to visually seperate compartments such as cytosol (c), periplasm (p) and extracellular (e)
  • Furthermore, individual species need to seperated in the same manneru
  • Include ability to hide inner subspaces (for example, hide intracellular when doing a community view)
• Dragging nodes:
  • Create seperate force layout for neighbourhood to be activated on drag [Visual]

Back-end

• Route for receiving model, and sending back model after FBA
  • store base model, as well as 'experiments'
  • experiments will store:
    • the framework which was used to achieve results
    • list of gene insertion/deletions, (reactions)
    • for each reaction, the flux
• compute 'flux deltas' for every experiment used with a specific model
• quee Python child processes, or find a better way to manage child processes. consider porting to a python based serverside framework such as Flask. how does Flask handle routes that will take on the order of minutes? Perhaps implement a job system.
• Users? to store and retrieve your 'experiments'

Community Flux Balance Analysis Pipeline

• Models have varying names for metabolites, or sometimes none and only the id. Develop a pipeline for creating and maintaining a standardized set of metabolites and reactions to be used across all models. Integrate this with a web application to increase cooperation and standardization
• cFBA framework
  • Given a list of n species and their respective genome scale metabolic models (following SBML format as either originally an .xml or later as a .json),
  • Develop a list of all reactions across all species that occur in the extracellular space, or e
  • For each species i in n, loop through every reaction in e. If a reaction contains a metabolite that is involved in a reaction from the 'global e reactions', append it into the model of that specie. This should take O(rxns_e * n) where rxns_e is the maximal number of extracellular reactions in a specie, and n is the number of species.
  • Perform FBA individual on each species i in n, optimizing for biomass production. The time this takes is proportional to the size of the linear problem to be solved. Further time analysis is necessary to accurately provide an upper bound on this process.
  • Using the results of FBA on each species, we have gathered a flux for each reaction in rxns_e of that specie. This will be different across the species; set a new upper and lower bound for each of this reactions by taking the average - SD and the average + SD. In this way, for all of the reactions which share extracellular metabolites, after this point, they will have the same lower and upper bounds.
  • Perform FBA on each species again. Optimizing for biomass, as before. Again, more structured time analysis is needed, however this should take similar time to the previous step.
  • Each biomass objective function for each species returns a numeric value. Use z-scoring to standardize these distributions, so that we may compute a 'relative biomass' value for each specie. The sum of all these values must be equal to 1.
  • Construct a single metabolic model representing the entire community. Each species will be assigned a 'compartment'. The upper and lower bounds of each reaction in each specie will be modified as per that specie's relative biomass. In this way, reactions belonging to a less important specie will have less effect (or more) on the optimization of the objective function, which will be biomass. The results of this cFBA analysis should coincide (to a degree) with experimentally observed community dynamics.

Dank Hackathon #1 Goals

First off, this is from the following paper,

Henson M, Hanly T. Dynamic flux balance analysis for synthetic microbial
communities. 2014. IET Syst. Biol. (8):5 214-229.

Although we will not doing any dFBA since it requires substrate uptake kinetics data, I think this outlines some of the grander goals that we will like to achieve with MetaFlux. A platform for viewing genome scale metabolic models, interacting with it live in an intuitive manner, and strong tools for analyzing results. Anyways, things to do tonight:

• Albert C., Sean
  • Are currently working on creating separate d3 force data sets so that you can display the entire cell as one node connected to extracellulars, and that once activated (perhaps via click or otherwise), the underlying data being drawn will change to reflect the intracellular reactions
• Albert X., Eric, Mark?
  • Working with MySQL, building the basics for flux value comparisons. Our data will have reaction objects, and those have a flux_value attribute. We need to store these flux values for each reaction, and then when another experiment is performed, quantitatively return the changes. For example, I run experiment 1 and get a flux of 2 for reaction A. Then I add/remove a reaction via the web interface, click "analyze" or have it run automatically or whatever, and then now the flux for reaction A is 3. So A should give me a delta of +1 between experiment 1 and 2. An experiment will be defined as an analysis on a model with a specific set of reactions/metabolites. But you can ignore all that for now, and just use random data. Perhaps drop some Math.rand() up in that shit. Essentially, given a mapping of objects and their numerical values, compute deltas for each number. We will integrate this with actual FBA later. This is necessary because only changing the thickness of arrows is quantitatively useless.
• Ghazal, Waleed
  • Anthony's gonna hate me for this kek, but I can't think of anything related to the core code base for you guys ATM, but.. I think it would be better if we provide the ability to use a cFBA approach that has already been developed in the literature in addition to our novel 'acFBA' framework. OptCom is a option, but is hella math heavy, and no code provided in the supplementary. If you guys could download the supplementary for the following article, and get it producing the data as it should, that would be useful. If you could make it work with data we have in our fba-database repo that would be hella hype. Though, as mentionned, we need to standardize ID's. So maybe just try to understand the data format their framework wants as input. The paper:

Khandelwal R, Olivier B, Roling W, et al. Community Flux Balance Analysis
for Microbial Consortia at Balanced Growth. 2013. PloS ONE 8(5).

If there is anything else you guys want to do instead, go ahead :)

License

MIT License